Automatically learning summary features is a recurring theme in the SBI literature. Fearnhead et al. [1]} present a regression scheme that approximates the posterior mean via a linear model and uses it as summary features for learning the posterior distribution. Partially exchangeable networks (PEN) [2]} explores invariances in time series to learn a set of summary features with the same loss function as [1]} and then approximates the posterior distribution using them. An important difference of our work is that we learn the summary statistics and the posterior distribution jointly, as done in [4]} but with a different summary feature extractor.
